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Title: Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

Abstract

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1345111
Report Number(s):
NREL/CP-5D00-67809
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the IEEE Power and Energy Conference, 23-24 February 2017, Champaign, Illinois
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; batteries; lithium-ion; modeling; analytical models; system integration; buildings; optimization

Citation Formats

Raszmann, Emma, Baker, Kyri, Shi, Ying, and Christensen, Dane. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint. United States: N. p., 2017. Web. doi:10.1109/PECI.2017.7935755.
Raszmann, Emma, Baker, Kyri, Shi, Ying, & Christensen, Dane. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint. United States. doi:10.1109/PECI.2017.7935755.
Raszmann, Emma, Baker, Kyri, Shi, Ying, and Christensen, Dane. Wed . "Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint". United States. doi:10.1109/PECI.2017.7935755. https://www.osti.gov/servlets/purl/1345111.
@article{osti_1345111,
title = {Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint},
author = {Raszmann, Emma and Baker, Kyri and Shi, Ying and Christensen, Dane},
abstractNote = {Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.},
doi = {10.1109/PECI.2017.7935755},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Feb 22 00:00:00 EST 2017},
month = {Wed Feb 22 00:00:00 EST 2017}
}

Conference:
Other availability
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